Wada Y, Koike Y, Vatikiotis-Bateson E, Kawato M
ATR Human Information Processing Research Laboratories, Soraku-gun, Kyoto, Japan.
Biol Cybern. 1995 Jun;73(1):15-25. doi: 10.1007/BF00199052.
We have previously proposed an optimal trajectory and control theory for continuous movements, such as reaching or cursive handwriting. According to Marr's three-level description of brain function, our theory can be summarized as follows: (1) The computational theory is the minimum torque-change model; (2) the intermediate representation of a pattern is given as a set of via-points extracted from an example pattern; and (3) algorithm and hardware are provided by FIRM, a neural network that can generate and control minimum torque-change trajectories. In this paper, we propose a computational theory for movement pattern recognition that is based on our theory for optimal movement pattern generation. The three levels of the description of brain function in the recognition theory are tightly coupled with those for pattern generation. In recognition, the generation process and the recognition process are actually two flows of information in opposite directions within a single functional unit. In our theory, if the input movement trajectory data are identical to the optimal movement pattern reconstructed from an intermediate representation of some symbol, the input data are recognized as that symbol. If an error exists between the movement trajectory data and the generated trajectory, the putative symbol is corrected, and the generation is repeated. In particular, we present concrete computational procedures for the recognition of connected cursive handwritten characters, as well as for the estimation of phonemic timing in natural speech. Our most important contribution is to demonstrate the computational realizability for the 'motor theory of movement pattern perception': the movement-pattern recognition process can be realized by actively recruiting the movement-pattern formation process. The way in which the formation process is utilized in pattern recognition in our theory suggests a duality between movement pattern formation and movement pattern perception.
我们之前提出了一种适用于连续运动（如伸手或草书书写）的最优轨迹与控制理论。根据马尔对大脑功能的三级描述，我们的理论可总结如下：（1）计算理论是最小扭矩变化模型；（2）模式的中间表示形式是从示例模式中提取的一组中间点；（3）算法和硬件由FIRM提供，FIRM是一种能够生成并控制最小扭矩变化轨迹的神经网络。在本文中，我们基于最优运动模式生成理论提出了一种运动模式识别的计算理论。识别理论中对大脑功能的三级描述与模式生成的描述紧密相关。在识别过程中，生成过程和识别过程实际上是在单个功能单元内沿相反方向的两个信息流。在我们的理论中，如果输入的运动轨迹数据与从某个符号的中间表示形式重建的最优运动模式相同，则输入数据被识别为该符号。如果运动轨迹数据与生成的轨迹之间存在误差，则对假定的符号进行校正，并重复生成过程。特别是，我们给出了识别连笔手写字符以及估计自然语音中音素时间的具体计算过程。我们最重要的贡献是证明了“运动模式感知的运动理论”的计算可实现性：运动模式识别过程可以通过积极调用运动模式形成过程来实现。我们的理论中在模式识别中利用形成过程的方式暗示了运动模式形成与运动模式感知之间的二元性。
